Retinal Vascular Network Topology Reconstruction and Artery/Vein Classification via Dominant Set Clustering

The estimation of vascular network topology in complex networks is important in understanding the relationship between vascular changes and a wide spectrum of diseases. Automatic classification of the retinal vascular trees into arteries and veins is of direct assistance to the ophthalmologist in terms of diagnosis and treatment of eye disease. However, it is challenging due to their projective ambiguity and subtle changes in appearance, contrast and geometry in the imaging process. In this paper, we propose a novel method that is capable of making the artery/vein (A/V) distinction in retinal color fundus images based on vascular network topological properties. To this end, we adapt the concept of dominant set clustering and formalize the retinal blood vessel topology estimation and the A/V classification as a pairwise clustering problem. The graph is constructed through image segmentation, skeletonization and identification of significant nodes. The edge weight is defined as the inverse Euclidean distance between its two end points in the feature space of intensity, orientation, curvature, diameter, and entropy. The reconstructed vascular network is classified into arteries and veins based on their intensity and morphology. The proposed approach has been applied to five public databases, INSPIRE, IOSTAR, VICAVR, DRIVE and WIDE, and achieved high accuracies of 95.1%, 94.2%, 93.8%, 91.1%, and 91.0%, respectively. Furthermore, we have made manual annotations of the blood vessel topologies for INSPIRE, IOSTAR, VICAVR, and DRIVE datasets, and these annotations are released for public access so as to facilitate researchers in the community

Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Execution

The Multi-Agent Path Finding (MAPF) problem involves planning collision-free paths for multiple agents in a shared environment. The majority of MAPF solvers rely on the assumption that an agent can arrive at a specific location at a specific timestep. However, real-world execution uncertainties can cause agents to deviate from this assumption, leading to collisions and deadlocks. Prior research solves this problem by having agents follow a Temporal Plan Graph (TPG), enforcing a consistent passing order at every location as defined in the MAPF plan. However, we show that TPGs are overly strict because, in some circumstances, satisfying the passing order requires agents to wait unnecessarily, leading to longer execution time. To overcome this issue, we introduce a new graphical representation called a Bidirectional Temporal Plan Graph (BTPG), which allows switching passing orders during execution to avoid unnecessary waiting time. We design two anytime algorithms for constructing a BTPG: BTPG-na\"ive and BTPG-optimized. Experimental results show that following BTPGs consistently outperforms following TPGs, reducing unnecessary waits by 8-20%.Comment: Accepted by AAAI-202

Dramatic differences in carbon dioxide adsorption and initial steps of reduction between silver and copper

Converting carbon dioxide (CO_2) into liquid fuels and synthesis gas is a world-wide priority. But there is no experimental information on the initial atomic level events for CO_2 electroreduction on the metal catalysts to provide the basis for developing improved catalysts. Here we combine ambient pressure X-ray photoelectron spectroscopy with quantum mechanics to examine the processes as Ag is exposed to CO_2 both alone and in the presence of H_2O at 298 K. We find that CO_2 reacts with surface O on Ag to form a chemisorbed species (O = CO_2^(δ−)). Adding H_2O and CO_2 then leads to up to four water attaching on O = CO_2^(δ−) and two water attaching on chemisorbed (b-)CO_2. On Ag we find a much more favorable mechanism involving the O = CO_2^(δ−) compared to that involving b-CO_2 on Cu. Each metal surface modifies the gas-catalyst interactions, providing a basis for tuning CO_2 adsorption behavior to facilitate selective product formations

Solid Solution Strengthened Fe Alloys

Iron (Fe)-based alloys (such as steel) are widely used structural materials in industry. Numerous methods have been applied to improve their mechanical properties. In this study, we used a technique know as magnetron sputtering to deposit various Fe-based binary alloy coatings to investigate the influence of solutes on solid solution hardening. Several factors contribute to the solid solution hardening of the alloys, such as composition, atomic radius, modulus, and lattice parameter. After preliminary calculations and analysis, we selected several solutes, including molybdenum (Mo), niobium (Nb), and zirconium (Zr). The compositions of solutes were varied to be 2.5, 5, 8 atomic %. Our nanoindentation hardness measurements show that among the three solid solution alloys, Fe-Zr has the highest hardness. The influences of solutes on microstructural and hardness evolution in these solid solution alloys are discussed

Development of a computational model to optimise adolescent idiopathic scoliosis correction

There is little consensus among spinal surgeons on the use of correction techniques in adolescent idiopathic scoliosis (AIS) surgery. For example, the choice of bilateral, concave or convex all-screw instrumentation to achieve optimal correction is debated [1]. As there is lack of biomechanical comparison for pre-operative guidance, the ultimate aim of this project is to assess the solutions for AIS correction across common scoliotic patterns encountered in clinical practice using computer simulations. Previous studies have used patient-specific models (developed from CT/MRI scans), so it is challenging to compare techniques for different cases and draw conclusions. This study aims to develop a validated generic spine model that excludes patient-specific features and can be repositioned to become scoliotic and used for optimising AIS correctio